Jitter is one of the premier impairments which inflict a heavy damage on the quality of service in wireless VoIP. To solve this issue, this paper proposes a new quality-based jitter buffer algorithm. An adaptive windo...
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ISBN:
(纸本)9781467329644;9781467329637
Jitter is one of the premier impairments which inflict a heavy damage on the quality of service in wireless VoIP. To solve this issue, this paper proposes a new quality-based jitter buffer algorithm. An adaptivewindowing algorithm is introduced to dynamically adjust the window size which indicates the numbers of packets used to estimate the future network delay and loss rete. When receiving a voice packet, the receiver firstly uses the variable-size windowalgorithm to update the window size. Then, the histogram of delay is established according to the remaining packets in window. Finally, E-Model is applied to evaluate the speech quality based on delay histogram. By searching for the maximum speech quality, the optimal buffer delay is obtained. Owe to the variable-size statistical window, both the accuracy of network delay prediction and the ability to deal with spikes have further improved. The experiment results show that the whole VoIP communication under our proposed algorithm not only suffers the smallest average delay and lowest packet loss, but also achieves the highest speech quality.
A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change...
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A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change Mining lies in its ability to serve both conformance checking and enhancement purposes;users can simultaneously detect changes and ensure process conformance using a single, integrated framework. In prior research, a configurable process Change Mining algorithm has been introduced. Combined with our proposed preprocessing and change log generation methods, this algorithm forms a complete framework for detecting and recording changes in a collection of event logs. Testing the framework on synthetic data revealed limitations in detecting changes in different types of variable fragments. Consequently, it is recommended that the preprocessing approach be enhanced by applying a filtering algorithm based on sliding and adaptivewindows. Our improved approach has been tested on various types of variable fragments to demonstrate its efficacy in enhancing Change Mining performance.
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